Corrosion detection for steel girder bridges using a convolutional neural network with focusing on brightness change for training data

Abstract

A convolutional neural network (CNN) has alos been gotten attention in the field of civil engineering in recent years, which is demonstrated useful inspection methods. A previous study has been reported in which the learning model of corrosion detector was developed using photographs of road bridge inspection results in Fukushima Prefecture as training data. However, the further expansion of training data has needed to improve the classification accuracy. In order to improve the accuracy of corrosion detection compared to previous studies, this study was focused on brightness changes of the preprocess in training data rather than geometric changes on images such as a rotation and flipping, which a common data augmentation method in the CNN. The training data was used the photographs of the condition of road bridge inspections in Fukushima Prefecture, which was preprocessed 90º rotation, left-right flipping and also contrast reduction and enhancement, and histogram flattening. The classifier for the corrosion detection was developed by applying those training data. As a result, we found that the classification accuracy of corrosion class could be maximized using the learning model trained data of contrast reduction preprocessed.